an Overview of New Methods for Optimizing the Reduce of Urban Traffic

Today, with the increase in the volume of traffic and the growth of travel, organizing and managing traffic is one of the necessities of urban medicine. Also, Traffic identification has become a challenging task in recent years. Recently, deep learning methods have been extensively studied for network traffic classification recently. Unfortunately, these models require a large amount of training data. Another challenge with most traffic classification methods is that the features must be extracted by an expert. In these methods, finding the desired features that lead to a better classification is very tedious and time-consuming. Therefore, new measures are needed to reduce urban traffic more than before. The purpose of this study is to investigate new methods of urban traffic control. Which is mostly based on previous research and analysis. Studies on new methods and models of traffic reduction, including technology, technology and intelligent systems, have been collected and reviewed in recent years. The results show that the use of these new technologies can estimate the volume of hidden traffic, improve traffic flow, accurately predict travel time operations and traffic volume, determine the appropriate service level, increase the capacity and efficiency of existing infrastructure. Transportation and specification of travel time, maximum queue length, queue stop, vehicle delay, stop delay and number of stops.